文献类型：期刊文献

英文题名：HyperDC: A Non-UniformHypergraph Framework for Dual-and Higher-Order Drug Combination Recommendation Across Diverse ComplexDiseases

作者：Yu, Hongbo[1];Chen, Xinyi[1];Song, Wenxiang[1];Xiong, Le[1];Li, Xinmin[1];Wang, Ze[1];Pan, Fei[1];Li, Weihua[1];Liu, Guixia[1];Gao, Feng[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2026

卷号：66

期号：14

起止页码：8561

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20263121193168);Scopus(收录号：2-s2.0-105045837876);WOS:【SCI-EXPANDED(收录号:WOS:001808953900001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant U23A20530) and the Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism.

语种：英文

外文关键词：Diagnosis - Disease control - Diseases - Drug discovery - Screening

摘要：Complex diseases are commonly characterized by dysregulation across multiple targets and pathways, making drug combinations an important therapeutic strategy. This creates a growing need for computational methods to prioritize candidate combinations, yet current methods face several practical challenges, including cross-disease application, unified prediction of dual- and multidrug combinations, lack of reliable negative labels, and prediction for low-resource diseases. In this study, we proposed HyperDC, a unified cross-disease drug combination recommendation framework. HyperDC constructs a nonuniform hypergraph based on clinical and knowledge-driven drug-disease association data, representing single drugs and dual- and multidrug combinations in a unified modeling space. It further integrates knowledge graph pretraining and adversarial negative sampling to enhance model discrimination across tasks with different difficulty levels. In the unified dual-drug benchmark and disease-specific prediction tasks, HyperDC outperformed representative methods by up to 12.8 and 30.0 percentage points, respectively. In fixed-anchor clinical ranking tasks, HyperDC showed superior performance across multiple top-ranked recall settings and preferentially recalled FDA-approved dual-drug combinations and clinically supported three-drug combinations. In the data-sparse metabolic dysfunction-associated steatohepatitis (MASH) scenario, HyperDC completed a full workflow from anchor-drug identification and combination partner prioritization to in vitro experimental validation: 70% of the top 10 single-drug candidates were supported by recent literature, and 4 of 6 tested combinations showed synergistic lipid-lowering and anti-inflammatory effects, yielding a hit rate of 66.7%. Overall, HyperDC may narrow the screening space, improve prioritization efficiency, and provide methodological support for the development of combination therapy strategies for complex diseases.